Abstract
In this contribution a genetic programming approach is
used to develop mathematical models of chemical process
systems. Having discussed genetic programming in
general, two examples are used to reveal the utility of
the technique. It is shown how the method can
discriminate between relevant and irrelevant process
inputs, evolving to yield parsimonious model structures
that accurately represent process characteristics. This
removes the need for restrictive assumptions about the
form of the data and the structure of the required
model. In addition, as the technique determines complex
nonlinear relationships in the data, non-intuitive
process features are revealed with comparative ease.
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